Overview

Dataset statistics

Number of variables21
Number of observations10127
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.6 MiB
Average record size in memory168.0 B

Variable types

Numeric17
Categorical4

Alerts

Customer_Age is highly overall correlated with Months_on_bookHigh correlation
Income_Category is highly overall correlated with GenderHigh correlation
Months_on_book is highly overall correlated with Customer_AgeHigh correlation
Credit_Limit is highly overall correlated with Avg_Open_To_BuyHigh correlation
Total_Revolving_Bal is highly overall correlated with Avg_Utilization_RatioHigh correlation
Avg_Open_To_Buy is highly overall correlated with Credit_Limit and 1 other fieldsHigh correlation
Total_Trans_Amt is highly overall correlated with Total_Trans_CtHigh correlation
Total_Trans_Ct is highly overall correlated with Total_Trans_AmtHigh correlation
Avg_Utilization_Ratio is highly overall correlated with Total_Revolving_Bal and 1 other fieldsHigh correlation
Gender is highly overall correlated with Income_CategoryHigh correlation
Card_Category is highly imbalanced (79.2%)Imbalance
CLIENTNUM has unique valuesUnique
Dependent_count has 904 (8.9%) zerosZeros
Contacts_Count_12_mon has 399 (3.9%) zerosZeros
Total_Revolving_Bal has 2470 (24.4%) zerosZeros
Avg_Utilization_Ratio has 2470 (24.4%) zerosZeros

Reproduction

Analysis started2023-11-13 10:28:37.911838
Analysis finished2023-11-13 10:29:22.168641
Duration44.26 seconds
Software versionydata-profiling vv4.6.1
Download configurationconfig.json

Variables

CLIENTNUM
Real number (ℝ)

UNIQUE 

Distinct10127
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.3917761 × 108
Minimum7.0808208 × 108
Maximum8.2834308 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2023-11-13T19:29:22.364590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7.0808208 × 108
5-th percentile7.0912039 × 108
Q17.1303677 × 108
median7.1792636 × 108
Q37.7314353 × 108
95-th percentile8.1421203 × 108
Maximum8.2834308 × 108
Range1.20261 × 108
Interquartile range (IQR)60106762

Descriptive statistics

Standard deviation36903783
Coefficient of variation (CV)0.049925462
Kurtosis-0.6156397
Mean7.3917761 × 108
Median Absolute Deviation (MAD)6347700
Skewness0.99560101
Sum7.4856516 × 1012
Variance1.3618892 × 1015
MonotonicityNot monotonic
2023-11-13T19:29:22.563140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
768805383 1
 
< 0.1%
711784908 1
 
< 0.1%
720133908 1
 
< 0.1%
803197833 1
 
< 0.1%
812222208 1
 
< 0.1%
757634583 1
 
< 0.1%
719362458 1
 
< 0.1%
789331908 1
 
< 0.1%
715616358 1
 
< 0.1%
806900508 1
 
< 0.1%
Other values (10117) 10117
99.9%
ValueCountFrequency (%)
708082083 1
< 0.1%
708083283 1
< 0.1%
708084558 1
< 0.1%
708085458 1
< 0.1%
708086958 1
< 0.1%
708095133 1
< 0.1%
708098133 1
< 0.1%
708099183 1
< 0.1%
708100533 1
< 0.1%
708103608 1
< 0.1%
ValueCountFrequency (%)
828343083 1
< 0.1%
828298908 1
< 0.1%
828294933 1
< 0.1%
828291858 1
< 0.1%
828288333 1
< 0.1%
828285858 1
< 0.1%
828281733 1
< 0.1%
828236133 1
< 0.1%
828227433 1
< 0.1%
828215508 1
< 0.1%

Attrition_Flag
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size79.2 KiB
2
8500 
1
1627 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10127
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 8500
83.9%
1 1627
 
16.1%

Length

2023-11-13T19:29:22.728463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-13T19:29:22.847277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 8500
83.9%
1 1627
 
16.1%

Most occurring characters

ValueCountFrequency (%)
2 8500
83.9%
1 1627
 
16.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10127
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 8500
83.9%
1 1627
 
16.1%

Most occurring scripts

ValueCountFrequency (%)
Common 10127
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 8500
83.9%
1 1627
 
16.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10127
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 8500
83.9%
1 1627
 
16.1%

Customer_Age
Real number (ℝ)

HIGH CORRELATION 

Distinct45
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.32596
Minimum26
Maximum73
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2023-11-13T19:29:22.985451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum26
5-th percentile33
Q141
median46
Q352
95-th percentile60
Maximum73
Range47
Interquartile range (IQR)11

Descriptive statistics

Standard deviation8.016814
Coefficient of variation (CV)0.1730523
Kurtosis-0.28861992
Mean46.32596
Median Absolute Deviation (MAD)6
Skewness-0.033605016
Sum469143
Variance64.269307
MonotonicityNot monotonic
2023-11-13T19:29:23.157473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
44 500
 
4.9%
49 495
 
4.9%
46 490
 
4.8%
45 486
 
4.8%
47 479
 
4.7%
43 473
 
4.7%
48 472
 
4.7%
50 452
 
4.5%
42 426
 
4.2%
51 398
 
3.9%
Other values (35) 5456
53.9%
ValueCountFrequency (%)
26 78
0.8%
27 32
 
0.3%
28 29
 
0.3%
29 56
 
0.6%
30 70
 
0.7%
31 91
0.9%
32 106
1.0%
33 127
1.3%
34 146
1.4%
35 184
1.8%
ValueCountFrequency (%)
73 1
 
< 0.1%
70 1
 
< 0.1%
68 2
 
< 0.1%
67 4
 
< 0.1%
66 2
 
< 0.1%
65 101
1.0%
64 43
0.4%
63 65
0.6%
62 93
0.9%
61 93
0.9%

Gender
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size79.2 KiB
2
5358 
1
4769 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10127
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row1
4th row2
5th row1

Common Values

ValueCountFrequency (%)
2 5358
52.9%
1 4769
47.1%

Length

2023-11-13T19:29:23.316589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-13T19:29:23.436692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 5358
52.9%
1 4769
47.1%

Most occurring characters

ValueCountFrequency (%)
2 5358
52.9%
1 4769
47.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10127
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 5358
52.9%
1 4769
47.1%

Most occurring scripts

ValueCountFrequency (%)
Common 10127
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 5358
52.9%
1 4769
47.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10127
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 5358
52.9%
1 4769
47.1%

Dependent_count
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3462032
Minimum0
Maximum5
Zeros904
Zeros (%)8.9%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2023-11-13T19:29:23.548202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile4
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2989083
Coefficient of variation (CV)0.55362142
Kurtosis-0.68301665
Mean2.3462032
Median Absolute Deviation (MAD)1
Skewness-0.020825536
Sum23760
Variance1.6871629
MonotonicityNot monotonic
2023-11-13T19:29:23.680967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 2732
27.0%
2 2655
26.2%
1 1838
18.1%
4 1574
15.5%
0 904
 
8.9%
5 424
 
4.2%
ValueCountFrequency (%)
0 904
 
8.9%
1 1838
18.1%
2 2655
26.2%
3 2732
27.0%
4 1574
15.5%
5 424
 
4.2%
ValueCountFrequency (%)
5 424
 
4.2%
4 1574
15.5%
3 2732
27.0%
2 2655
26.2%
1 1838
18.1%
0 904
 
8.9%

Education_Level
Real number (ℝ)

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3411672
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2023-11-13T19:29:23.802409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q34
95-th percentile6
Maximum7
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.6008549
Coefficient of variation (CV)0.47913043
Kurtosis-0.48766448
Mean3.3411672
Median Absolute Deviation (MAD)1
Skewness0.31060313
Sum33836
Variance2.5627363
MonotonicityNot monotonic
2023-11-13T19:29:23.932774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
4 3128
30.9%
2 2013
19.9%
1 1519
15.0%
3 1487
14.7%
5 1013
 
10.0%
6 516
 
5.1%
7 451
 
4.5%
ValueCountFrequency (%)
1 1519
15.0%
2 2013
19.9%
3 1487
14.7%
4 3128
30.9%
5 1013
 
10.0%
6 516
 
5.1%
7 451
 
4.5%
ValueCountFrequency (%)
7 451
 
4.5%
6 516
 
5.1%
5 1013
 
10.0%
4 3128
30.9%
3 1487
14.7%
2 2013
19.9%
1 1519
15.0%

Marital_Status
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size79.2 KiB
3
4687 
2
3943 
1
749 
4
748 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10127
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row2
3rd row3
4th row1
5th row3

Common Values

ValueCountFrequency (%)
3 4687
46.3%
2 3943
38.9%
1 749
 
7.4%
4 748
 
7.4%

Length

2023-11-13T19:29:24.084622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-13T19:29:24.208828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 4687
46.3%
2 3943
38.9%
1 749
 
7.4%
4 748
 
7.4%

Most occurring characters

ValueCountFrequency (%)
3 4687
46.3%
2 3943
38.9%
1 749
 
7.4%
4 748
 
7.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10127
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 4687
46.3%
2 3943
38.9%
1 749
 
7.4%
4 748
 
7.4%

Most occurring scripts

ValueCountFrequency (%)
Common 10127
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 4687
46.3%
2 3943
38.9%
1 749
 
7.4%
4 748
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10127
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 4687
46.3%
2 3943
38.9%
1 749
 
7.4%
4 748
 
7.4%

Income_Category
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0857115
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2023-11-13T19:29:24.345381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.4746392
Coefficient of variation (CV)0.47789277
Kurtosis-0.91071317
Mean3.0857115
Median Absolute Deviation (MAD)1
Skewness0.46776016
Sum31249
Variance2.1745608
MonotonicityNot monotonic
2023-11-13T19:29:24.483616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2 3561
35.2%
3 1790
17.7%
5 1535
15.2%
4 1402
 
13.8%
1 1112
 
11.0%
6 727
 
7.2%
ValueCountFrequency (%)
1 1112
 
11.0%
2 3561
35.2%
3 1790
17.7%
4 1402
 
13.8%
5 1535
15.2%
6 727
 
7.2%
ValueCountFrequency (%)
6 727
 
7.2%
5 1535
15.2%
4 1402
 
13.8%
3 1790
17.7%
2 3561
35.2%
1 1112
 
11.0%

Card_Category
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size79.2 KiB
1
9436 
2
 
555
3
 
116
4
 
20

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10127
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 9436
93.2%
2 555
 
5.5%
3 116
 
1.1%
4 20
 
0.2%

Length

2023-11-13T19:29:24.640978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-13T19:29:24.795652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9436
93.2%
2 555
 
5.5%
3 116
 
1.1%
4 20
 
0.2%

Most occurring characters

ValueCountFrequency (%)
1 9436
93.2%
2 555
 
5.5%
3 116
 
1.1%
4 20
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10127
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 9436
93.2%
2 555
 
5.5%
3 116
 
1.1%
4 20
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 10127
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 9436
93.2%
2 555
 
5.5%
3 116
 
1.1%
4 20
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10127
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 9436
93.2%
2 555
 
5.5%
3 116
 
1.1%
4 20
 
0.2%

Months_on_book
Real number (ℝ)

HIGH CORRELATION 

Distinct44
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.928409
Minimum13
Maximum56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2023-11-13T19:29:25.038185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile22
Q131
median36
Q340
95-th percentile50
Maximum56
Range43
Interquartile range (IQR)9

Descriptive statistics

Standard deviation7.9864163
Coefficient of variation (CV)0.22228695
Kurtosis0.40010012
Mean35.928409
Median Absolute Deviation (MAD)4
Skewness-0.10656536
Sum363847
Variance63.782846
MonotonicityNot monotonic
2023-11-13T19:29:25.274380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
36 2463
24.3%
37 358
 
3.5%
34 353
 
3.5%
38 347
 
3.4%
39 341
 
3.4%
40 333
 
3.3%
31 318
 
3.1%
35 317
 
3.1%
33 305
 
3.0%
30 300
 
3.0%
Other values (34) 4692
46.3%
ValueCountFrequency (%)
13 70
0.7%
14 16
 
0.2%
15 34
 
0.3%
16 29
 
0.3%
17 39
 
0.4%
18 58
0.6%
19 63
0.6%
20 74
0.7%
21 83
0.8%
22 105
1.0%
ValueCountFrequency (%)
56 103
1.0%
55 42
 
0.4%
54 53
 
0.5%
53 78
0.8%
52 62
 
0.6%
51 80
0.8%
50 96
0.9%
49 141
1.4%
48 162
1.6%
47 171
1.7%

Total_Relationship_Count
Real number (ℝ)

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8125802
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2023-11-13T19:29:25.419467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median4
Q35
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.5544079
Coefficient of variation (CV)0.40770496
Kurtosis-1.0061305
Mean3.8125802
Median Absolute Deviation (MAD)1
Skewness-0.16245241
Sum38610
Variance2.4161838
MonotonicityNot monotonic
2023-11-13T19:29:25.667588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 2305
22.8%
4 1912
18.9%
5 1891
18.7%
6 1866
18.4%
2 1243
12.3%
1 910
 
9.0%
ValueCountFrequency (%)
1 910
 
9.0%
2 1243
12.3%
3 2305
22.8%
4 1912
18.9%
5 1891
18.7%
6 1866
18.4%
ValueCountFrequency (%)
6 1866
18.4%
5 1891
18.7%
4 1912
18.9%
3 2305
22.8%
2 1243
12.3%
1 910
 
9.0%

Months_Inactive_12_mon
Real number (ℝ)

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3411672
Minimum0
Maximum6
Zeros29
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2023-11-13T19:29:25.816377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median2
Q33
95-th percentile4
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.0106224
Coefficient of variation (CV)0.4316746
Kurtosis1.0985226
Mean2.3411672
Median Absolute Deviation (MAD)1
Skewness0.63306113
Sum23709
Variance1.0213576
MonotonicityNot monotonic
2023-11-13T19:29:26.034359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
3 3846
38.0%
2 3282
32.4%
1 2233
22.0%
4 435
 
4.3%
5 178
 
1.8%
6 124
 
1.2%
0 29
 
0.3%
ValueCountFrequency (%)
0 29
 
0.3%
1 2233
22.0%
2 3282
32.4%
3 3846
38.0%
4 435
 
4.3%
5 178
 
1.8%
6 124
 
1.2%
ValueCountFrequency (%)
6 124
 
1.2%
5 178
 
1.8%
4 435
 
4.3%
3 3846
38.0%
2 3282
32.4%
1 2233
22.0%
0 29
 
0.3%

Contacts_Count_12_mon
Real number (ℝ)

ZEROS 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4553175
Minimum0
Maximum6
Zeros399
Zeros (%)3.9%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2023-11-13T19:29:26.220860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median2
Q33
95-th percentile4
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1062251
Coefficient of variation (CV)0.45054261
Kurtosis0.00086265663
Mean2.4553175
Median Absolute Deviation (MAD)1
Skewness0.011005626
Sum24865
Variance1.2237341
MonotonicityNot monotonic
2023-11-13T19:29:26.358774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
3 3380
33.4%
2 3227
31.9%
1 1499
14.8%
4 1392
13.7%
0 399
 
3.9%
5 176
 
1.7%
6 54
 
0.5%
ValueCountFrequency (%)
0 399
 
3.9%
1 1499
14.8%
2 3227
31.9%
3 3380
33.4%
4 1392
13.7%
5 176
 
1.7%
6 54
 
0.5%
ValueCountFrequency (%)
6 54
 
0.5%
5 176
 
1.7%
4 1392
13.7%
3 3380
33.4%
2 3227
31.9%
1 1499
14.8%
0 399
 
3.9%

Credit_Limit
Real number (ℝ)

HIGH CORRELATION 

Distinct6205
Distinct (%)61.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8631.9537
Minimum1438.3
Maximum34516
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2023-11-13T19:29:26.586800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1438.3
5-th percentile1438.51
Q12555
median4549
Q311067.5
95-th percentile34516
Maximum34516
Range33077.7
Interquartile range (IQR)8512.5

Descriptive statistics

Standard deviation9088.7767
Coefficient of variation (CV)1.0529223
Kurtosis1.8089893
Mean8631.9537
Median Absolute Deviation (MAD)2593
Skewness1.6667258
Sum87415795
Variance82605861
MonotonicityNot monotonic
2023-11-13T19:29:26.775754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34516 508
 
5.0%
1438.3 507
 
5.0%
9959 18
 
0.2%
15987 18
 
0.2%
23981 12
 
0.1%
2490 11
 
0.1%
6224 11
 
0.1%
3735 11
 
0.1%
7469 10
 
0.1%
2069 8
 
0.1%
Other values (6195) 9013
89.0%
ValueCountFrequency (%)
1438.3 507
5.0%
1439 2
 
< 0.1%
1440 1
 
< 0.1%
1441 2
 
< 0.1%
1442 1
 
< 0.1%
1443 3
 
< 0.1%
1446 1
 
< 0.1%
1449 2
 
< 0.1%
1451 2
 
< 0.1%
1452 2
 
< 0.1%
ValueCountFrequency (%)
34516 508
5.0%
34496 1
 
< 0.1%
34458 1
 
< 0.1%
34427 1
 
< 0.1%
34198 1
 
< 0.1%
34173 1
 
< 0.1%
34162 1
 
< 0.1%
34140 1
 
< 0.1%
34058 1
 
< 0.1%
34010 1
 
< 0.1%

Total_Revolving_Bal
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1974
Distinct (%)19.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1162.8141
Minimum0
Maximum2517
Zeros2470
Zeros (%)24.4%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2023-11-13T19:29:27.166866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1359
median1276
Q31784
95-th percentile2517
Maximum2517
Range2517
Interquartile range (IQR)1425

Descriptive statistics

Standard deviation814.98734
Coefficient of variation (CV)0.70087503
Kurtosis-1.1459918
Mean1162.8141
Median Absolute Deviation (MAD)591
Skewness-0.14883725
Sum11775818
Variance664204.36
MonotonicityNot monotonic
2023-11-13T19:29:27.438850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2470
 
24.4%
2517 508
 
5.0%
1965 12
 
0.1%
1480 12
 
0.1%
1434 11
 
0.1%
1664 11
 
0.1%
1720 11
 
0.1%
1590 10
 
0.1%
1542 10
 
0.1%
1528 10
 
0.1%
Other values (1964) 7062
69.7%
ValueCountFrequency (%)
0 2470
24.4%
132 1
 
< 0.1%
134 1
 
< 0.1%
145 1
 
< 0.1%
154 1
 
< 0.1%
157 1
 
< 0.1%
159 2
 
< 0.1%
168 2
 
< 0.1%
170 1
 
< 0.1%
186 1
 
< 0.1%
ValueCountFrequency (%)
2517 508
5.0%
2514 3
 
< 0.1%
2513 1
 
< 0.1%
2512 2
 
< 0.1%
2511 1
 
< 0.1%
2509 2
 
< 0.1%
2508 2
 
< 0.1%
2507 4
 
< 0.1%
2506 1
 
< 0.1%
2505 3
 
< 0.1%

Avg_Open_To_Buy
Real number (ℝ)

HIGH CORRELATION 

Distinct6813
Distinct (%)67.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7469.1396
Minimum3
Maximum34516
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2023-11-13T19:29:27.696723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile480.3
Q11324.5
median3474
Q39859
95-th percentile32183.4
Maximum34516
Range34513
Interquartile range (IQR)8534.5

Descriptive statistics

Standard deviation9090.6853
Coefficient of variation (CV)1.2170994
Kurtosis1.7986173
Mean7469.1396
Median Absolute Deviation (MAD)2665
Skewness1.6616965
Sum75639977
Variance82640560
MonotonicityNot monotonic
2023-11-13T19:29:27.895818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1438.3 324
 
3.2%
34516 98
 
1.0%
31999 26
 
0.3%
787 8
 
0.1%
701 7
 
0.1%
713 7
 
0.1%
953 7
 
0.1%
463 7
 
0.1%
990 6
 
0.1%
788 6
 
0.1%
Other values (6803) 9631
95.1%
ValueCountFrequency (%)
3 1
< 0.1%
10 1
< 0.1%
14 2
< 0.1%
15 1
< 0.1%
24 1
< 0.1%
28 1
< 0.1%
29 1
< 0.1%
36 1
< 0.1%
39 2
< 0.1%
41 2
< 0.1%
ValueCountFrequency (%)
34516 98
1.0%
34362 1
 
< 0.1%
34302 1
 
< 0.1%
34300 1
 
< 0.1%
34297 1
 
< 0.1%
34286 1
 
< 0.1%
34238 1
 
< 0.1%
34227 1
 
< 0.1%
34140 1
 
< 0.1%
34119 1
 
< 0.1%

Total_Amt_Chng_Q4_Q1
Real number (ℝ)

Distinct1158
Distinct (%)11.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.75994065
Minimum0
Maximum3.397
Zeros5
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2023-11-13T19:29:28.086744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.463
Q10.631
median0.736
Q30.859
95-th percentile1.103
Maximum3.397
Range3.397
Interquartile range (IQR)0.228

Descriptive statistics

Standard deviation0.21920677
Coefficient of variation (CV)0.28845248
Kurtosis9.9935012
Mean0.75994065
Median Absolute Deviation (MAD)0.114
Skewness1.7320634
Sum7695.919
Variance0.048051608
MonotonicityNot monotonic
2023-11-13T19:29:28.335078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.791 36
 
0.4%
0.712 34
 
0.3%
0.743 34
 
0.3%
0.718 33
 
0.3%
0.735 33
 
0.3%
0.744 32
 
0.3%
0.699 32
 
0.3%
0.722 32
 
0.3%
0.731 31
 
0.3%
0.631 31
 
0.3%
Other values (1148) 9799
96.8%
ValueCountFrequency (%)
0 5
< 0.1%
0.01 1
 
< 0.1%
0.018 1
 
< 0.1%
0.046 1
 
< 0.1%
0.061 2
 
< 0.1%
0.072 1
 
< 0.1%
0.101 1
 
< 0.1%
0.12 1
 
< 0.1%
0.153 1
 
< 0.1%
0.163 1
 
< 0.1%
ValueCountFrequency (%)
3.397 1
< 0.1%
3.355 1
< 0.1%
2.675 1
< 0.1%
2.594 1
< 0.1%
2.368 1
< 0.1%
2.357 1
< 0.1%
2.316 1
< 0.1%
2.282 1
< 0.1%
2.275 1
< 0.1%
2.271 1
< 0.1%

Total_Trans_Amt
Real number (ℝ)

HIGH CORRELATION 

Distinct5033
Distinct (%)49.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4404.0863
Minimum510
Maximum18484
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2023-11-13T19:29:28.555799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum510
5-th percentile1283.3
Q12155.5
median3899
Q34741
95-th percentile14212
Maximum18484
Range17974
Interquartile range (IQR)2585.5

Descriptive statistics

Standard deviation3397.1293
Coefficient of variation (CV)0.77135847
Kurtosis3.8940234
Mean4404.0863
Median Absolute Deviation (MAD)1308
Skewness2.0410034
Sum44600182
Variance11540487
MonotonicityNot monotonic
2023-11-13T19:29:28.937816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4253 11
 
0.1%
4509 11
 
0.1%
4518 10
 
0.1%
2229 10
 
0.1%
4220 9
 
0.1%
4869 9
 
0.1%
4037 9
 
0.1%
4313 9
 
0.1%
4498 9
 
0.1%
4042 9
 
0.1%
Other values (5023) 10031
99.1%
ValueCountFrequency (%)
510 1
< 0.1%
530 1
< 0.1%
563 1
< 0.1%
569 1
< 0.1%
594 1
< 0.1%
596 1
< 0.1%
597 1
< 0.1%
602 1
< 0.1%
615 1
< 0.1%
643 1
< 0.1%
ValueCountFrequency (%)
18484 1
< 0.1%
17995 1
< 0.1%
17744 1
< 0.1%
17634 1
< 0.1%
17628 1
< 0.1%
17498 1
< 0.1%
17437 1
< 0.1%
17390 1
< 0.1%
17350 1
< 0.1%
17258 1
< 0.1%

Total_Trans_Ct
Real number (ℝ)

HIGH CORRELATION 

Distinct126
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.858695
Minimum10
Maximum139
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2023-11-13T19:29:29.168054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile28
Q145
median67
Q381
95-th percentile105
Maximum139
Range129
Interquartile range (IQR)36

Descriptive statistics

Standard deviation23.47257
Coefficient of variation (CV)0.36190322
Kurtosis-0.36716324
Mean64.858695
Median Absolute Deviation (MAD)17
Skewness0.15367307
Sum656824
Variance550.96156
MonotonicityNot monotonic
2023-11-13T19:29:29.389385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
81 208
 
2.1%
71 203
 
2.0%
75 203
 
2.0%
69 202
 
2.0%
82 202
 
2.0%
76 198
 
2.0%
77 197
 
1.9%
70 193
 
1.9%
74 190
 
1.9%
78 190
 
1.9%
Other values (116) 8141
80.4%
ValueCountFrequency (%)
10 4
 
< 0.1%
11 2
 
< 0.1%
12 4
 
< 0.1%
13 5
 
< 0.1%
14 9
 
0.1%
15 16
0.2%
16 13
0.1%
17 13
0.1%
18 23
0.2%
19 11
0.1%
ValueCountFrequency (%)
139 1
 
< 0.1%
138 1
 
< 0.1%
134 1
 
< 0.1%
132 1
 
< 0.1%
131 6
0.1%
130 5
< 0.1%
129 6
0.1%
128 10
0.1%
127 12
0.1%
126 10
0.1%

Total_Ct_Chng_Q4_Q1
Real number (ℝ)

Distinct830
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.71222238
Minimum0
Maximum3.714
Zeros7
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2023-11-13T19:29:29.574658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.368
Q10.582
median0.702
Q30.818
95-th percentile1.069
Maximum3.714
Range3.714
Interquartile range (IQR)0.236

Descriptive statistics

Standard deviation0.23808609
Coefficient of variation (CV)0.33428617
Kurtosis15.689293
Mean0.71222238
Median Absolute Deviation (MAD)0.119
Skewness2.0640306
Sum7212.676
Variance0.056684987
MonotonicityNot monotonic
2023-11-13T19:29:29.775465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.667 171
 
1.7%
1 166
 
1.6%
0.5 161
 
1.6%
0.75 156
 
1.5%
0.6 113
 
1.1%
0.8 101
 
1.0%
0.714 92
 
0.9%
0.833 85
 
0.8%
0.778 69
 
0.7%
0.625 63
 
0.6%
Other values (820) 8950
88.4%
ValueCountFrequency (%)
0 7
0.1%
0.028 1
 
< 0.1%
0.029 1
 
< 0.1%
0.038 1
 
< 0.1%
0.053 1
 
< 0.1%
0.059 2
 
< 0.1%
0.062 1
 
< 0.1%
0.074 1
 
< 0.1%
0.077 3
< 0.1%
0.091 3
< 0.1%
ValueCountFrequency (%)
3.714 1
 
< 0.1%
3.571 1
 
< 0.1%
3.5 1
 
< 0.1%
3.25 1
 
< 0.1%
3 2
< 0.1%
2.875 1
 
< 0.1%
2.75 1
 
< 0.1%
2.571 1
 
< 0.1%
2.5 3
< 0.1%
2.429 1
 
< 0.1%

Avg_Utilization_Ratio
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct964
Distinct (%)9.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.27489355
Minimum0
Maximum0.999
Zeros2470
Zeros (%)24.4%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2023-11-13T19:29:30.046634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.023
median0.176
Q30.503
95-th percentile0.793
Maximum0.999
Range0.999
Interquartile range (IQR)0.48

Descriptive statistics

Standard deviation0.27569147
Coefficient of variation (CV)1.0029026
Kurtosis-0.79497195
Mean0.27489355
Median Absolute Deviation (MAD)0.176
Skewness0.718008
Sum2783.847
Variance0.076005786
MonotonicityNot monotonic
2023-11-13T19:29:30.316454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2470
 
24.4%
0.073 44
 
0.4%
0.057 33
 
0.3%
0.048 32
 
0.3%
0.06 30
 
0.3%
0.061 29
 
0.3%
0.045 29
 
0.3%
0.059 28
 
0.3%
0.069 28
 
0.3%
0.053 27
 
0.3%
Other values (954) 7377
72.8%
ValueCountFrequency (%)
0 2470
24.4%
0.004 1
 
< 0.1%
0.005 1
 
< 0.1%
0.006 3
 
< 0.1%
0.007 1
 
< 0.1%
0.008 2
 
< 0.1%
0.009 1
 
< 0.1%
0.01 1
 
< 0.1%
0.011 1
 
< 0.1%
0.012 4
 
< 0.1%
ValueCountFrequency (%)
0.999 1
 
< 0.1%
0.995 1
 
< 0.1%
0.994 1
 
< 0.1%
0.992 1
 
< 0.1%
0.99 1
 
< 0.1%
0.988 1
 
< 0.1%
0.987 1
 
< 0.1%
0.985 1
 
< 0.1%
0.984 1
 
< 0.1%
0.983 4
< 0.1%

Interactions

2023-11-13T19:29:18.308243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-13T19:28:39.312108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-13T19:28:42.790275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-13T19:28:45.143652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-13T19:28:47.829405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-13T19:28:50.810700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-13T19:28:52.838946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-13T19:28:54.749603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-13T19:28:56.774246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-13T19:28:59.079156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-13T19:29:01.136562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-13T19:29:03.737663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-13T19:29:06.044385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-13T19:29:08.903707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-13T19:29:11.421633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-13T19:29:13.663813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-13T19:29:15.886447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-13T19:29:18.479783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-13T19:28:39.431273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-13T19:28:42.924449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-13T19:28:45.279932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-13T19:28:48.021795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-13T19:28:50.945645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-13T19:28:52.950448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-13T19:28:54.865233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-13T19:28:56.892403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-13T19:28:59.196786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-13T19:29:01.281675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-13T19:29:03.868825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-13T19:29:06.182664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-11-13T19:28:54.289203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-13T19:28:56.289373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-13T19:28:58.590746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-13T19:29:00.629278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-13T19:29:03.160626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-13T19:29:05.429419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-13T19:29:07.870484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-13T19:29:10.849419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-13T19:29:13.095370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-13T19:29:15.374794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-13T19:29:17.653976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-13T19:29:20.393239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-13T19:28:41.329668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-13T19:28:44.738644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-13T19:28:47.251443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-13T19:28:50.072885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-13T19:28:52.483509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-13T19:28:54.407302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-13T19:28:56.412625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-13T19:28:58.714577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-13T19:29:00.753641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-13T19:29:03.305070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-13T19:29:05.603976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-13T19:29:08.427258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-13T19:29:11.018753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-13T19:29:13.239317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-13T19:29:15.503621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-13T19:29:17.795596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-13T19:29:20.571412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-13T19:28:41.455152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-13T19:28:44.867569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-13T19:28:47.441425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-13T19:28:50.495528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-13T19:28:52.602816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-13T19:28:54.523188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-13T19:28:56.532223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-13T19:28:58.834679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-13T19:29:00.872346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-13T19:29:03.448140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-13T19:29:05.751973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-13T19:29:08.563574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-13T19:29:11.155223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-13T19:29:13.375811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-13T19:29:15.631960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-13T19:29:17.944424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-13T19:29:20.731545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-13T19:28:41.601759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-13T19:28:45.007983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-13T19:28:47.658492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-13T19:28:50.674927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-13T19:28:52.723123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-13T19:28:54.639776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-13T19:28:56.656415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-13T19:28:58.958439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-13T19:29:01.002025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-13T19:29:03.606020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-13T19:29:05.898488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-13T19:29:08.760486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-13T19:29:11.286811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-13T19:29:13.523143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-13T19:29:15.760640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-13T19:29:18.128491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-11-13T19:29:30.494733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
CLIENTNUMCustomer_AgeDependent_countEducation_LevelIncome_CategoryMonths_on_bookTotal_Relationship_CountMonths_Inactive_12_monContacts_Count_12_monCredit_LimitTotal_Revolving_BalAvg_Open_To_BuyTotal_Amt_Chng_Q4_Q1Total_Trans_AmtTotal_Trans_CtTotal_Ct_Chng_Q4_Q1Avg_Utilization_RatioAttrition_FlagGenderMarital_StatusCard_Category
CLIENTNUM1.0000.017-0.0040.0050.0300.1110.014-0.0080.0110.0140.0030.0110.024-0.0020.0060.0160.0070.0480.0140.0060.000
Customer_Age0.0171.000-0.144-0.0060.0200.769-0.0140.044-0.0140.0020.014-0.002-0.071-0.039-0.054-0.0400.0110.0240.0000.0820.021
Dependent_count-0.004-0.1441.0000.0070.062-0.115-0.036-0.009-0.0410.051-0.0040.054-0.0260.0580.0530.009-0.0350.0210.0000.0370.018
Education_Level0.005-0.0060.0071.000-0.0090.001-0.0020.012-0.006-0.001-0.0140.002-0.0130.0000.001-0.012-0.0080.0250.0110.0110.000
Income_Category0.0300.0200.062-0.0091.0000.017-0.003-0.0160.0210.4090.0340.397-0.002-0.077-0.069-0.025-0.1920.0280.8390.0080.053
Months_on_book0.1110.769-0.1150.0010.0171.000-0.0140.057-0.0080.0070.0060.008-0.054-0.029-0.039-0.034-0.0040.0190.0110.0430.013
Total_Relationship_Count0.014-0.014-0.036-0.002-0.003-0.0141.000-0.0070.061-0.0590.012-0.0710.026-0.279-0.2270.0240.0650.1660.0000.0220.067
Months_Inactive_12_mon-0.0080.044-0.0090.012-0.0160.057-0.0071.0000.030-0.028-0.043-0.016-0.019-0.032-0.051-0.047-0.0270.1960.0190.0070.000
Contacts_Count_12_mon0.011-0.014-0.041-0.0060.021-0.0080.0610.0301.0000.023-0.0450.033-0.021-0.167-0.168-0.093-0.0590.2390.0590.0070.010
Credit_Limit0.0140.0020.051-0.0010.4090.007-0.059-0.0280.0231.0000.1310.9310.0210.0280.034-0.011-0.4170.0320.4390.0260.335
Total_Revolving_Bal0.0030.014-0.004-0.0140.0340.0060.012-0.043-0.0450.1311.000-0.1540.0360.0180.0400.0780.7090.4020.0330.0120.019
Avg_Open_To_Buy0.011-0.0020.0540.0020.3970.008-0.071-0.0160.0330.931-0.1541.0000.0070.0220.022-0.040-0.6860.0190.4400.0280.337
Total_Amt_Chng_Q4_Q10.024-0.071-0.026-0.013-0.002-0.0540.026-0.019-0.0210.0210.0360.0071.0000.1350.0850.3020.0330.1840.0440.0530.024
Total_Trans_Amt-0.002-0.0390.0580.000-0.077-0.029-0.279-0.032-0.1670.0280.0180.0220.1351.0000.8800.2230.0190.3250.2470.1040.154
Total_Trans_Ct0.006-0.0540.0530.001-0.069-0.039-0.227-0.051-0.1680.0340.0400.0220.0850.8801.0000.2330.0400.4580.1630.0990.109
Total_Ct_Chng_Q4_Q10.016-0.0400.009-0.012-0.025-0.0340.024-0.047-0.093-0.0110.078-0.0400.3020.2230.2331.0000.0940.3140.0500.0300.000
Avg_Utilization_Ratio0.0070.011-0.035-0.008-0.192-0.0040.065-0.027-0.059-0.4170.709-0.6860.0330.0190.0400.0941.0000.2410.2790.0270.149
Attrition_Flag0.0480.0240.0210.0250.0280.0190.1660.1960.2390.0320.4020.0190.1840.3250.4580.3140.2411.0000.0360.0170.000
Gender0.0140.0000.0000.0110.8390.0110.0000.0190.0590.4390.0330.4400.0440.2470.1630.0500.2790.0361.0000.0090.084
Marital_Status0.0060.0820.0370.0110.0080.0430.0220.0070.0070.0260.0120.0280.0530.1040.0990.0300.0270.0170.0091.0000.028
Card_Category0.0000.0210.0180.0000.0530.0130.0670.0000.0100.3350.0190.3370.0240.1540.1090.0000.1490.0000.0840.0281.000

Missing values

2023-11-13T19:29:21.461438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-11-13T19:29:21.954973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

CLIENTNUMAttrition_FlagCustomer_AgeGenderDependent_countEducation_LevelMarital_StatusIncome_CategoryCard_CategoryMonths_on_bookTotal_Relationship_CountMonths_Inactive_12_monContacts_Count_12_monCredit_LimitTotal_Revolving_BalAvg_Open_To_BuyTotal_Amt_Chng_Q4_Q1Total_Trans_AmtTotal_Trans_CtTotal_Ct_Chng_Q4_Q1Avg_Utilization_Ratio
07688053832451323413951312691.077711914.01.3351144421.6250.061
1818770008249254221446128256.08647392.01.5411291333.7140.105
2713982108251134351364103418.003418.02.5941887202.3330.000
3769911858240242121343413313.02517796.01.4051171202.3330.760
4709106358240133341215104716.004716.02.175816282.5000.000
5713061558244124331363124010.012472763.01.3761088240.8460.311
68103472082511413634661334516.0226432252.01.9751330310.7220.066
78189062082321021422722229081.0139627685.02.2041538360.7140.048
87109305082371332413652022352.0251719835.03.3551350241.1820.113
97196615582481242513663311656.016779979.01.5241441320.8820.144
CLIENTNUMAttrition_FlagCustomer_AgeGenderDependent_countEducation_LevelMarital_StatusIncome_CategoryCard_CategoryMonths_on_bookTotal_Relationship_CountMonths_Inactive_12_monContacts_Count_12_monCredit_LimitTotal_Revolving_BalAvg_Open_To_BuyTotal_Amt_Chng_Q4_Q1Total_Trans_AmtTotal_Trans_CtTotal_Ct_Chng_Q4_Q1Avg_Utilization_Ratio
101177125034082571243514063417925.0190916016.00.712174981110.8200.106
10118713755458150111151366349959.09529007.00.82510310631.1000.096
101197168936831552332114743314657.0251712140.00.1666009530.5140.172
101207108411832541122413452013940.0210911831.00.660155771140.7540.151
10121713899383256214221504143688.06063082.00.570145961200.7910.164
10122772366833250124231403234003.018512152.00.703154761170.8570.462
10123710638233141121431254234277.021862091.00.8048764690.6830.511
10124716506083144212321365345409.005409.00.81910291600.8180.000
10125717406983130124131364335281.005281.00.5358395620.7220.000
101267143372331432243222562410388.019618427.00.70310294610.6490.189